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.ipynb-checkpoint
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{
"cells": [
{
"cell_type": "code",
"execution_count": 100,
"id": "dc84019e",
"metadata": {},
"outputs": [],
"source": [
"# importing files and dataset\n",
"import numpy as np\n",
"import pandas as pd\n",
"from datetime import date\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import metrics\n",
"from sklearn.metrics import r2_score\n",
"\n",
"student_por = pd.read_csv(r'C:\\Users\\janha\\Desktop\\StudentPerformanceAnalysis\\data\\student-por.csv', sep=';')\n",
"student_mat = pd.read_csv(r'C:\\Users\\janha\\Desktop\\StudentPerformanceAnalysis\\data\\student-mat.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 101,
"id": "4c3572ed",
"metadata": {},
"outputs": [
{
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" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
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"2 GP F 15 U LE3 T 1 1 at_home other ... \n",
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"4 GP F 16 U GT3 T 3 3 other other ... \n",
"\n",
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"4 4 3 2 1 2 5 0 11 13 13 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"student_por.head()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "7a1bd68c",
"metadata": {},
"outputs": [
{
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" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
"0 GP F 18 U GT3 A 4 4 at_home teacher ... \n",
"1 GP F 17 U GT3 T 1 1 at_home other ... \n",
"2 GP F 15 U LE3 T 1 1 at_home other ... \n",
"3 GP F 15 U GT3 T 4 2 health services ... \n",
"4 GP F 16 U GT3 T 3 3 other other ... \n",
"\n",
" famrel freetime goout Dalc Walc health absences G1 G2 G3 \n",
"0 4 3 4 1 1 3 6 5 6 6 \n",
"1 5 3 3 1 1 3 4 5 5 6 \n",
"2 4 3 2 2 3 3 10 7 8 10 \n",
"3 3 2 2 1 1 5 2 15 14 15 \n",
"4 4 3 2 1 2 5 4 6 10 10 \n",
"\n",
"[5 rows x 33 columns]"
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},
"execution_count": 102,
"metadata": {},
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}
],
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]
},
{
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"id": "5b024024",
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"output_type": "stream",
"text": [
"Number of Mathematics Student Data: 395\n",
"Number of Portuguese Student Data: 649\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Number of Mathematics Student Data:\",len(student_mat))\n",
"print(\"Number of Portuguese Student Data:\",len(student_por))\n",
"list(student_mat)==list(student_por)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "dfb4b56c",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>school</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>address</th>\n",
" <th>famsize</th>\n",
" <th>Pstatus</th>\n",
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" <th>Mjob</th>\n",
" <th>Fjob</th>\n",
" <th>...</th>\n",
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" <th>goout</th>\n",
" <th>Dalc</th>\n",
" <th>Walc</th>\n",
" <th>health</th>\n",
" <th>absences</th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>F</td>\n",
" <td>18</td>\n",
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" <td>GT3</td>\n",
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" <td>4</td>\n",
" <td>4</td>\n",
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" <td>LE3</td>\n",
" <td>T</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
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" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>16</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
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" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>MS</td>\n",
" <td>M</td>\n",
" <td>20</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>A</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
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" <td>11</td>\n",
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" <td>9</td>\n",
" <td>9</td>\n",
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" <tr>\n",
" <th>391</th>\n",
" <td>MS</td>\n",
" <td>M</td>\n",
" <td>17</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
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" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>MS</td>\n",
" <td>M</td>\n",
" <td>21</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>MS</td>\n",
" <td>M</td>\n",
" <td>18</td>\n",
" <td>R</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>MS</td>\n",
" <td>M</td>\n",
" <td>19</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>at_home</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>395 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n",
"0 GP F 18 U GT3 A 4 4 at_home teacher \n",
"1 GP F 17 U GT3 T 1 1 at_home other \n",
"2 GP F 15 U LE3 T 1 1 at_home other \n",
"3 GP F 15 U GT3 T 4 2 health services \n",
"4 GP F 16 U GT3 T 3 3 other other \n",
".. ... .. ... ... ... ... ... ... ... ... \n",
"390 MS M 20 U LE3 A 2 2 services services \n",
"391 MS M 17 U LE3 T 3 1 services services \n",
"392 MS M 21 R GT3 T 1 1 other other \n",
"393 MS M 18 R LE3 T 3 2 services other \n",
"394 MS M 19 U LE3 T 1 1 other at_home \n",
"\n",
" ... famrel freetime goout Dalc Walc health absences G1 G2 G3 \n",
"0 ... 4 3 4 1 1 3 6 5 6 6 \n",
"1 ... 5 3 3 1 1 3 4 5 5 6 \n",
"2 ... 4 3 2 2 3 3 10 7 8 10 \n",
"3 ... 3 2 2 1 1 5 2 15 14 15 \n",
"4 ... 4 3 2 1 2 5 4 6 10 10 \n",
".. ... ... ... ... ... ... ... ... .. .. .. \n",
"390 ... 5 5 4 4 5 4 11 9 9 9 \n",
"391 ... 2 4 5 3 4 2 3 14 16 16 \n",
"392 ... 5 5 3 3 3 3 3 10 8 7 \n",
"393 ... 4 4 1 3 4 5 0 11 12 10 \n",
"394 ... 3 2 3 3 3 5 5 8 9 9 \n",
"\n",
"[395 rows x 33 columns]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"student_mat.dropna(inplace=True)\n",
"data = student_mat.copy()\n",
"# student_por.dropna(inplace=True)\n",
"# data = pd.concat([student_mat,student_por], axis=0)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "e93a8445",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['teacher', 'other', 'services', 'health', 'at_home'], dtype=object)"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"student_mat.Fjob.unique()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "71865a95",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"school: ['GP' 'MS'] -> [0 1]\n",
"sex: ['F' 'M'] -> [0 1]\n",
"address: ['U' 'R'] -> [0 1]\n",
"famsize: ['GT3' 'LE3'] -> [1 0]\n",
"Pstatus: ['A' 'T'] -> [0 1]\n",
"Mjob: ['at_home' 'health' 'other' 'services' 'teacher'] -> [0 2 4 3 1]\n",
"Mjob: ['teacher' 'other' 'services' 'health' 'at_home'] -> [1 4 3 2 0]\n",
"reason: ['course' 'other' 'home' 'reputation'] -> [2 3 0 1]\n",
"guardian: ['mother' 'father' 'other'] -> [0 1 2]\n",
"schoolsup: ['yes' 'no'] -> [1 0]\n",
"famsup: ['no' 'yes'] -> [0 1]\n",
"paid: ['no' 'yes'] -> [0 1]\n",
"activities: ['no' 'yes'] -> [0 1]\n",
"nursery: ['yes' 'no'] -> [1 0]\n",
"higher: ['yes' 'no'] -> [1 0]\n",
"internet: ['no' 'yes'] -> [0 1]\n",
"romantic: ['no' 'yes'] -> [0 1]\n"
]
}
],
"source": [
"# data preprocessing\n",
"\n",
"#school\n",
"data.loc[data['school']=='GP','school'] = 0\n",
"data.loc[data['school']=='MS','school'] = 1\n",
"print('school: ',student_mat.school.unique(),' -> ',data.school.unique())\n",
"\n",
"#sex\n",
"data.loc[data['sex']=='F','sex'] = 0\n",
"data.loc[data['sex']=='M','sex'] = 1\n",
"print('sex: ',student_mat.sex.unique(),' -> ',data.sex.unique())\n",
"\n",
"#address\n",
"data.loc[data['address']=='U','address'] = 0\n",
"data.loc[data['address']=='R','address'] = 1\n",
"print('address: ',student_mat.address.unique(),' -> ',data.address.unique())\n",
"\n",
"#family size \n",
"data.loc[data['famsize']=='LE3','famsize'] = 0\n",
"data.loc[data['famsize']=='GT3','famsize'] = 1\n",
"print('famsize: ',student_mat.famsize.unique(),' -> ',data.famsize.unique())\n",
"\n",
"#parent's cohabitaiion\n",
"data.loc[data['Pstatus']=='A','Pstatus'] = 0\n",
"data.loc[data['Pstatus']=='T','Pstatus'] = 1\n",
"print('Pstatus: ',student_mat.Pstatus.unique(),' -> ',data.Pstatus.unique())\n",
"\n",
"#mother's job\n",
"data.loc[data['Mjob']=='at_home','Mjob'] = 0\n",
"data.loc[data['Mjob']=='teacher','Mjob'] = 1\n",
"data.loc[data['Mjob']=='health','Mjob'] = 2\n",
"data.loc[data['Mjob']=='services','Mjob'] = 3\n",
"data.loc[data['Mjob']=='other','Mjob'] = 4\n",
"print('Mjob: ',student_mat.Mjob.unique(),' -> ',data.Mjob.unique())\n",
"\n",
"#father's job\n",
"data.loc[data['Fjob']=='at_home','Fjob'] = 0\n",
"data.loc[data['Fjob']=='teacher','Fjob'] = 1\n",
"data.loc[data['Fjob']=='health','Fjob'] = 2\n",
"data.loc[data['Fjob']=='services','Fjob'] = 3\n",
"data.loc[data['Fjob']=='other','Fjob'] = 4\n",
"print('Mjob: ',student_mat.Fjob.unique(),' -> ',data.Fjob.unique())\n",
"\n",
"#reason\n",
"data.loc[data['reason']=='home','reason'] = 0\n",
"data.loc[data['reason']=='reputation','reason'] = 1\n",
"data.loc[data['reason']=='course','reason'] = 2\n",
"data.loc[data['reason']=='other','reason'] = 3\n",
"print('reason: ',student_mat.reason.unique(),' -> ',data.reason.unique())\n",
"\n",
"#guardian\n",
"data.loc[data['guardian']=='mother','guardian'] = 0\n",
"data.loc[data['guardian']=='father','guardian'] = 1\n",
"data.loc[data['guardian']=='other','guardian'] = 2\n",
"print('guardian: ',student_mat.guardian.unique(),' -> ',data.guardian.unique())\n",
"\n",
"#school support\n",
"data.loc[data['schoolsup']=='no','schoolsup'] = 0\n",
"data.loc[data['schoolsup']=='yes','schoolsup'] = 1\n",
"print('schoolsup: ',student_mat.schoolsup.unique(),' -> ',data.schoolsup.unique())\n",
"\n",
"#family support\n",
"data.loc[data['famsup']=='no','famsup'] = 0\n",
"data.loc[data['famsup']=='yes','famsup'] = 1\n",
"print('famsup: ',student_mat.famsup.unique(),' -> ',data.famsup.unique())\n",
"\n",
"#paid\n",
"data.loc[data['paid']=='no','paid'] = 0\n",
"data.loc[data['paid']=='yes','paid'] = 1\n",
"print('paid: ',student_mat.paid.unique(),' -> ',data.paid.unique())\n",
"\n",
"#activities\n",
"data.loc[data['activities']=='no','activities'] = 0\n",
"data.loc[data['activities']=='yes','activities'] = 1\n",
"print('activities: ',student_mat.activities.unique(),' -> ',data.activities.unique())\n",
"\n",
"#nursery\n",
"data.loc[data['nursery']=='no','nursery'] = 0\n",
"data.loc[data['nursery']=='yes','nursery'] = 1\n",
"print('nursery: ',student_mat.nursery.unique(),' -> ',data.nursery.unique())\n",
"\n",
"#higher edu\n",
"data.loc[data['higher']=='no','higher'] = 0\n",
"data.loc[data['higher']=='yes','higher'] = 1\n",
"print('higher: ',student_mat.higher.unique(),' -> ',data.higher.unique())\n",
"\n",
"#internet \n",
"data.loc[data['internet']=='no','internet'] = 0\n",
"data.loc[data['internet']=='yes','internet'] = 1\n",
"print('internet: ',student_mat.internet.unique(),' -> ',data.internet.unique())\n",
"\n",
"#romantic \n",
"data.loc[data['romantic']=='no','romantic'] = 0\n",
"data.loc[data['romantic']=='yes','romantic'] = 1\n",
"print('romantic: ',student_mat.romantic.unique(),' -> ',data.romantic.unique())\n"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "b8b9236e",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",